The proliferation of mobile electronic devices allows the instantaneous collection of tremendous amount of digital data in our daily lives. Much of these digital data is meant to be processed and eventually be displayed in graphical and text formats, such as digital newsfeeds, instant image captures, and text messages. The processing that converts these raw digital data collected or captured in their binary and/or machine-readable formats into human-readable text may involve certain data decoding steps, other special conversion steps such as optical character recognition (OCR), and/or language translation. However, these data processing procedures are not error free, and often result in erroneous characters and words, or even illegible text. Thus, an additional step of language error detection and corrections, such as spell checking and auto-correction, is needed.
Conventional spell checking and auto-correction are resource intensive computer operations that take large amount of central process unit (CPU) processing cycles and volatile memory space. In a mobile computing device, such as a smartphone, both CPU capacity and memory space are much more limited in comparison to regular computers. On the other hand, the user experience of spell checking and auto-correction demands real-time performance and high level of accuracy. Therefore, there is a need for a better system and/or method for organizing and processing input text generated from raw data and dictionaries used to spell check and auto-correct the input text that has more efficient use of computing resources.